Embodiments of the present technique relate generally to computer vision applications, and more particularly to video based fall detection.
Unintentional falls are one of the most complex and costly health issues facing elderly people. Recent studies show that approximately one in every three adults age 65 years or older falls each year, and about 30 percent of these falls result in serious injuries. Particularly, people who experience a fall event at home may remain on the ground for an extended period of time as help may not be immediately available. The studies indicate a high mortality rate amongst such people who remain on the ground for an hour or more after a fall.
Fall detection (FD), therefore, has become a major focus of healthcare facilities. Conventionally, healthcare facilities employ nursing staff to monitor a person around the clock. In settings such as assisted living or independent community life, however, the desire for privacy and the associated expense render such constant monitoring undesirable. Accordingly, several techniques have been introduced to effectively monitor and detect fall events. These techniques may be broadly classified into four categories—embedded sensor based FD systems, community or social alarm based FD systems, acoustic sensor-based FD systems and video sensor based FD systems.
The embedded sensor based FD systems may typically entail use of physical motion sensors such as accelerometers and gyroscopes. Similarly, the social alarm based FD systems may use a wearable device such as a medallion or a wristwatch that includes a pushbutton. Such sensor based and social alarm based FD systems may be successful only if the individual wears the motion sensing devices at all times and is physically and cognitively able to activate the alarm when an emergency arises. Further, the acoustic based FD systems may include microphones that may be used to detect falls by analyzing frequency components of vibrations caused by an impact of a human body with the ground. However, the acoustic based FD systems are best suited for detecting heavy impacts and may be less useful in situations where a resident has slid out of a chair or otherwise become stuck on the floor without a rapid decent and heavy impact.
Accordingly, in recent times, video based systems are being widely investigated for efficient fall detection. The video based FD systems process images of the person's motion in real time to evaluate if detected horizontal and vertical velocities corresponding to the person's motion indicate a fall event. Only a portion of falls, however, is heavy falls having high horizontal and vertical velocities. The remaining falls, characterized by low horizontal and vertical velocities, thus, may not be robustly detected by the video based FD systems. Further, determination of the horizontal and vertical velocities while detecting human falls involves use of complex computations and classification algorithms, thereby requiring higher processing power and expensive equipment. The computations become even more complicated when data from multiple video acquisition devices positioned at various positions in an FD environment is used for fall detection. Conventional video-based FD systems, thus, fail to provide cost effectiveness or ease of implementation.
Moreover, use of such video based FD systems typically involves acquisition of personally identifiable information leading to numerous privacy concerns. Specifically, constant monitoring and acquisition of identifiable videos may be considered by many people to be an intrusion of their privacy.
It may therefore be desirable to develop an effective system and method for detecting high-risk movements, especially human fall events. Additionally, there is a need for a relatively inexpensive FD system that may be easily mounted for effectively detecting the fall events in a wide area with a fairly low instance of false alarms. Further, it may be desirable for the FD system to be able to adapt to different configurations of objects and furniture disposed in the wide area, while non-intrusively yet reliably detecting a wide variety of falls.